Method for real-time extraction of ocean bottom properties

ABSTRACT

A method for characterizing ocean bottom properties in real-time by  explong the multipath structure of reverberation to extract values for bottom loss and the bottom scattering coefficient. The bottom parameters are extracted from a measured reverberation energy envelope generated from the reverberation returns of one or more pings from a sonar system. The measured reverberation envelope is compared to a reference reverberation model to identify a period of time in which the measured reverberation energy envelope exhibits properties that allow the extraction of bottom parameters. The bottom parameters are then extracted by direct comparison of the measured reverberation to the reference reverberation model within the time period or by iteratively changing the value of bottom loss or bottom scatter coefficient and recalculating the reference reverberation for the identified time period until the reference reverberation matches the representative reverberation. The accuracy of the reference reverberation model is maintained by continuously monitoring the current sonar state configuration and the current environmental parameters including bottom depth, sound speed profile, and wind speed and rebuilding the model should either the sonar configuration or the environmental parameters change in a manner which would cause a change in the model.

STATEMENT OF GOVERNMENT INTEREST

The invention described herein may be manufactured and used by or forthe Government of the United States of America for governmental purposeswithout the payment of any royalties thereon or therefor.

CROSS-REFERENCE TO RELATED PATENT APPLICATION

This patent application is co-pending with a related patent applicationSer. No. 80/330,143, filed on Oct. 18, 1994, entitled SYSTEM ANDPROCESSOR FOR REAL-TIME EXTRACTION OF OCEAN BOTTOM PROPERTIES, Navy CaseNo. 76411, of which Judith Bishop, Michael T. Sundvik and David W.Grande are co-inventors.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates to a processor for extracting ocean bottomproperties in real-time. More particularly, the invention relates to amethod for the extraction of the bottom loss and the bottom scatteringstrength coefficient in real-time.

2. Description of the Prior Art

In bottom limited environments, sonar signals must bounce off the bottomand the surface several times in order to increase the detection rangeand the performance of the sonar system. As is known in the art, sonarperformance and detection range are often estimated or predicted bycomputing active signal excess. Active signal excess, by definition, isthe received signal to noise ratio divided by the signal to noise ratiorequired for detection, for a given probability of false alarm anddetection. The results of the active signal excess calculations are thenused to select the optimum mode for the sonar system including bestcurrent mode, pulse length, and range scale settings. However, toaccurately predict the range at which a return signal can be detected aswell as sonar performance, one must be able to accurately predict theamount of attenuation of the sonar signal due to the absorption of soundby the bottom as well as the reflection of sound by the bottom. Overestimating or under estimating the amount of bottom loss or bottomscattering greatly affects the calculation of active signal excess.Thus, to optimize the active signal excess performance prediction inbottom limited environments, it is necessary to accurately predict thebottom loss and the bottom scattering coefficient. Additionally,accurately determining bottom loss and bottom scattering coefficientenables one to distinguish and differentiate bottom types.

Current methods for determining values for the bottom loss and thebottom scattering coefficient suffer from one or more disadvantages ordefects which limit their application for many uses. For example, onemethod for determining bottom loss relies on estimates for bottom lossand bottom scattering obtained by surveying the areas of the bottom,obtaining bottom samples (sediment cores) or obtaining extensive sonardata and accurately measuring values for the bottom loss and bottomscattering using the sediment cores or the extensive sonar data.However, such a method requires that large areas of the bottom besurveyed, often requiring a large amount of time, and cannot beperformed in situ or in real-time. Additionally, in many shallow bottomlimited areas, currents, biologics or human activities can change thethickness and make-up of the bottom materially affecting the bottomloss.

Other methods, such as that described in U.S. Pat. No. 3,555,499,determine a single value for attenuation due to the ocean bottom bycomparing magnitude of transmitted sonar signals with the magnitude ofthe returns. However, such methods do not generate independent valuesfor the bottom loss and the bottom scattering coefficient nor do suchmethods compute bottom attenuation as a function of grazing angle.Additionally, the method of 3,555,499 does not consider environmentalparameters affecting depth and absorption measurements such as the soundspeed profile. There is no method for the real-time determination ofvalues for bottom loss and bottom scattering coefficient needed toaccurately predict sonar performance. Such a method would be a welcomeaddition to the art.

SUMMARY OF THE INVENTION

Accordingly, it is a general purpose and object of the present inventionto provide a method for the extraction of ocean bottom properties.

Another object of the present invention is the provision of a method toextract values for the bottom loss and the bottom scattering coefficientin real-time.

A further object of the present invention is the provision of a methodto determine values for bottom loss and bottom scattering coefficient inreal-time by processing measured reverberation.

Yet a further object of the present invention is the provision of amethod for characterizing ocean bottom properties in real-time usingtime correlated sonar, navigational, and environmental data.

These and other objects are accomplished with the present invention byproviding a method for extraction of ocean bottom parameters inreal-time. The bottom parameters are obtained by obtaining arepresentative reverberation energy envelope generated from thereverberation returns of one or more pings from a sonar system andcomparing this representative reverberation envelope to a referencereverberation/eigenray model to identify a period of time in which therepresentative reverberation energy envelope exhibits properties whichallows the extraction of bottom parameters. The bottom parameters canthen be extracted by direct comparison of the representativereverberation to the reference reverberation within the time period orby iteratively changing the value of the bottom loss or the bottomscattering coefficient and recalculating the reference reverberation forthe identified time period until the reference reverberation matches therepresentative reverberation. The accuracy of the eigenray and referencereverberation models is maintained by continuously monitoring thecurrent sonar state configuration and the current environmentalparameters including bottom depth, sound speed profile, and wind speedand rebuilding both models should either the sonar configuration or theenvironmental parameters change in a manner which would cause a changein either model.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the invention and many of the attendantadvantages thereto will be readily appreciated as the same becomesbetter understood by reference to the following detailed descriptionwhen considered in conjunction with the accompanying drawings whereinlike reference numerals and symbols designate identical or correspondingparts throughout the several views and wherein:

FIG. 1 shows a block diagram of a method for the real-timecharacterization of ocean bottom properties in accordance with thepresent invention;

FIG. 2 shows three sample reverberation time histories (reverberationlevel, in dBs, as a function of time) computed by the method of thepresent invention;

FIG. 3 is a block diagram of the functional units of a processor forreal-time extraction of ocean properties for use with the method of thepresent invention;

FIG. 4 is a schematic diagram showing the hardware elements, systemenclosures and data paths, along with the functions performed by each,of a processor for real-time extraction of ocean properties; and

FIG. 5 is a block diagram of a system for the extraction of ocean bottomproperties in real-time.

DESCRIPTION OF THE PREFERRED EMBODIMENT

Referring now to FIG. 1, there is shown a block diagram outlining thesteps of a method for characterizing ocean bottom properties inreal-time in accordance with the present invention. A navigationalsystem and various environmental sensors (not shown) provide input 10 todata collection task 16. Input 10 provides information such as shipposition coordinates, ship heading, ship speed, relative wind directionand velocity, and water temperature and depth data. A sonar system (notshown) provides sonar array signal 12 corresponding to a measuredacoustic signal as well as sonar state data 14 containing suchinformation as depression elevation angle, frequency, pulse length andrepetition rate.

In data collection task 16, measured and estimated data from thenavigational system, environmental sensors and the sonar system ismonitored. Data collection task 16 polls the data, performs anynecessary data conversion, and generates input data stream 18.Typically, data stream 18 will be a single data stream comprisingsampled navigational, environmental and sonar data multiplexed withacoustic signal data. Preferably, data collection is performed atcontinuous periodic rates based upon the expected rate of change of thedata and upon the consistency and accuracy of instantaneous datasamples. Reasonable periodic rates are once every second fornavigational, environmental and sonar state data sampling. The measuredacoustic signal should be sampled at a rate in excess of the Nyquistfrequency to ensure that an accurate digital representation of theacoustic signal is obtained.

Data correlation task 20 groups the data collected in task 16 by typeand time correlates the data. Data correlation task 20 receives anddemultiplexes input data stream 18, grouping the data as either acousticsignal data (time series sequence of multibit digital numbersrepresenting the acoustic signal) or system/sensor data. Thesystem/sensor data can be further grouped into raw data blockscontaining navigational system, environmental sensor or sonar statedata. Data correlation task 20 time correlates the data such that thesystem/sensor data samples and the acoustic signal data samples can betracked and related to one another over a common time domain.

Reverberation processing task 22 receives acoustic signal data and sonarstate data from data correlation task 20. Reverberation processing task22 processes the time domain digital beam data into a reverberationenergy envelope in the frequency band of interest. Each ping from thesonar system produces an acoustic return which can be processed into asingle reverberation energy envelope. The raw digital signal data isprocessed into a reverberation envelope using standard energy envelopeprocessing techniques. The waveform data is basebanded and filtered. Thefiltered waveform data undergoes own doppler nullification (ODN)processing to remove the frequency shift in the waveform resulting fromthe ship's motion. Fast Fourier transform processing is used to obtainthe frequency domain function of the waveform from which thereverberation energy envelope is then computed. In computing areverberation energy envelope for use in extraction of bottomparameters, the time step between data points on the reverberationenergy curve is preferably equal to the pulse length. That is, if thepulse length is 10 ms, the time step between data points in thereverberation envelope is 10 ms (an output rate of 100 samples/sec).

Sonar state parameter processing task 24 processes the raw sonar statedata into valid system state data for use by other processing steps. Theraw sonar state data relates information regarding the configuration,condition, and status of the sonar system. Task 24 monitors the rawsonar state data and converts the data into valid system state data suchas source angle, source heading, pulse length, frequency, transmitterbeam pattern and receiver beam pattern. To reduce the amount ofprocessing performed in computing the transmitter and receiver beampatterns, task 24 precomputes a set of transmitter and receiver beampatterns. These beam patterns are precomputed, based on the full phasesand element locations of the sonar, for a set of steering directionssuch as 0, 10, 20 and 30 degrees down. A pre-integration of the beampatterns in azimuth is performed to reduce the full three dimensionalbeam patterns to an equivalent two-dimensional pattern (beam functionvs. vertical angle) which gives the full fidelity of three dimensionalbeam pattern in the model, but allows for a significant improvement inreverberation calculation time required for real-time extraction.

Task 24 also monitors the sonar system state parameters to determinewhether the current sonar system configuration is a valid configurationfor the parameter extraction method to be used and to ascertain whetherany sonar state parameter has changed. If task 24 determines that thecurrent configuration is not a valid configuration or that, although theconfiguration is valid, one or more sonar state parameters have changed,task 24 notifies reverberation and environmental parameter postprocessing task 28, eigenray modeling task 30, reference reverberationmodeling task 32, and bottom parameter characterization task 34 of thesonar system state change.

Environmental and navigational parameter processing task 26 uses the rawenvironmental and navigational data collected in task 16 to computevalid environment parameters such as sound speed profile, correctedbottom depth, true wind speed and true source bearing.

A sound speed profile is generated by calculating the speed of sound asa function of depth. The speed of sound can be calculated if thetemperature, pressure and salinity of the water are known by using thesevalues in one of several well known formulas such as the Del Grosso,Kuwahara, Medwin, or Wilson equations. The Del Grosso empirical soundspeed relation is given in Del Grosso, New Equation for the Speed ofSound in Natural Waters, J. Acoust. Soc. of Amer., vol. 56, no. 4, 1974,pp. 1084-1091, incorporated herein by reference.

Values for temperature, pressure and salinity as a function of depth canbe measured or estimated using a combination of an expendablebathythermogram (XBT) and a velocity meter or similar devices. A drop ofan XBT can provide values for temperature and pressure as a function ofdepth. A towed device such as a velocity meter containing temperatureand pressure sensors can obtain measurements of temperature, pressure,and sound velocity, at a single point in the water column. The depth ofthe velocity meter can be determined from the pressure measurements. Thesalinity can then be determined by solving one of the above sound speedequations such as the Del Grosso sound speed equation for salinity usingthe temperature, pressure and sound velocity measurements.

Utilizing a known sound speed equation such as the Del Grosso relation,task 26 generate the sound speed profile by calculating the speed ofsound at each point in the water column from the surface to the bottom.The sound speed profile can be accurately approximated by calculatingthe speed of sound at each layer in a set of contiguous layers from thesurface to the bottom if the width of the layers is made sufficientlynarrow. A reasonable width for such layers is one meter. The sound speedprofile is generated using data from a recent XBT drop and one or morevalues of salinity determined by a towed device. When calculating thesound speed profile using a series of layers, the raw XBT data can besmoothed to remove or average data points arising from duplicate XBTreadings within a single layer. As water depths increase and the latestsound speed profile no longer reaches the bottom, and until the next XBTis available, the profile can be extended to the new bottom depth bysimple extrapolation of the deepest available sound speed to the presentbottom depth. Since the bottom parameter extraction process is designedfor shallow water, no provision has been made to merge measured profileswith a database of deep sound speed profiles.

The corrected bottom depth (true depth) can be determined by measuringthe nominal, uncorrected depth by using a fathometer or the like andconverting the uncorrected depth to true depth by integrating over thesound speed profile to convert travel time to corrected depth from thefathometer to the ocean bottom. True depth from the surface to thebottom can then be obtained by including the depth of the fathometertransducer.

Several factors including noise, a poorly-reflective bottom, or shipmotion can introduce errors into the fathometer readings and affect theaccuracy of the readings. To compensate for such outliers in thefathometer readings, the true depth is calculated periodically, andafter a statistically significant number of depth values have beencalculated, the median value is ascertained, using the most recentvalues, after each calculation of true depth. That is, after a new valueof true depth is calculated, the new value is added to the list ofvalues and the oldest value is dropped from the list. A new median valueof this updated list is then determined. It is anticipated that thedepth can be accurately assessed by calculating the true depth everysecond and determining the median value using the sixty most recentdepth calculations.

True wind speed is required to estimate when the sea surface loss issignificant enough to have an effect on reverberation decay in theshallow water environment. The true wind speed is determined from acombination of the ship's course and speed information in combinationwith relative wind speed and direction measurements. The relative windspeed and direction can be obtained by using a commercially availableweather station, an interface to the ship's anemometers, or the like.The relative wind speed and direction are averaged over a short periodof time, such as one-minute. An appropriate vector sum of the averagedrelative wind and ship's motion (bearing and speed) determines true windspeed. The ship's bearing and speed can be determined from GlobalPositioning System (GPS) satellites. The GPS satellites provide the shipwith accurate navigational data and exact position coordinates. Thedifference between two GPS coordinate readings can be used to accuratelydetermine the ship's true course and speed.

Reverberation and environmental parameter post processing task 28attempts to detect environmental and acoustic boundaries by correlatingcharacteristic changes in the reverberation energy envelope with theenvironmental parameters calculated in task 26, as well as by monitoringthe environmental parameters for change. Determining environmental andacoustic propagation boundaries aids in maintaining and computing validsets of acoustic model eigenrays and a valid reference reverberationmodel needed to perform bottom parameter extraction processing.

Both eigenray and reference reverberation modeling are processorintensive operations taking a long time to complete relative to theother processing tasks. Reducing the amount of time used to generate theeigenray and reference reverberation models increases the amount of timeavailable to perform extraction processing. One way to reduce the amountof time-required to complete eigenray and reference reverberationmodeling is to reduce the number of times that these models are built.To perform bottom parameter extraction using measured reverberationreturns, the eigenray and reference reverberation models only need to berebuilt when there is a change in the environment having a large enougheffect on the acoustic propagation to affect the reverberation returns.Detecting these environmental and acoustic boundaries which effectreverberation returns and building the eigenray and reverberation modelsonly when such boundaries are identified, minimizes the number of timesthat the eigenray and reverberation models will be built.

To detect characteristic changes in the reverberation energy envelope,task 28 statistically analyzes and compares a "current" reverberationenvelope to a "baseline" (reference) reverberation envelope. To obtainthe baseline reverberation energy, task 28 computes an averagereverberation envelope from a given number of reverberation envelopes ina specified beam. This baseline average reverberation envelope can becomputed using a single envelope (single ping) as computed inreverberation processing task 22 or using several envelopes. Typically,several envelopes are used in computing the baseline averagereverberation envelope to remove fine differences, such as those arisingfrom minor variations in the environment or from random or bias errorsattributable to the sonar system, leaving only major differences whichcan be associated with environmental boundaries. In selecting the numberof envelopes to average, consideration should be given to the pulselength, the repetition rate, and the area sonified along with therequirement that the reverberation envelopes be taken from returns oversubstantially the same ocean bottom area. A reasonable number ofenvelopes to average is 6.

After the baseline reverberation envelope is acquired, new averagereverberation envelopes are calculated as the ship travels through thewater. The most recently acquired average reverberation envelope islabelled the current reverberation envelope and is used in statisticalanalysis and comparison with the baseline reverberation envelope. Thenumber of envelopes averaged is determined using the same considerationsas outlined above for calculating the baseline reverberation envelope.Preferably, the same number of envelopes used to create the baselineaverage are used to create the new average reverberation envelope.

Characteristic changes in the reverberation energy envelope areidentified using several common statistical analysis methods. Task 28compares the baseline reverberation average to the current reverberationaverage using the overall mean difference as a function of time and achi squared analysis. Both analyses are performed over the entirereverberation average. Task 28 also divides both the current andbaseline average into several segments and performs mean difference andchi squared analyses on these individual segments. Task 28 can alsocompare the current to the baseline reverberation average by identifyingchanges in the variance, in the onset of reverberation or in theduration of reverberation. As is obvious to one skilled in the art,other analysis methods can be substituted for or performed in additionto the chi squared and mean difference analyses.

If any one of the above analyses identifies variations between the twoaverages, post processing task 28 correlates the navigational,environmental, and sonar parameters with the statistical data in anattempt to identify an environmental boundary. Task 28 first identifiesthe portions of the current and baseline reverberation averagesexhibiting changes, and, using this information, task 28 identifies thetime period over which these portions were obtained. Knowing the timeperiods enables task 28 to obtain the values of the environmentalparameters during those periods. Task 28 compares the obtained values ofthe environmental parameters with the values provided by task 26 todetermine whether the changes in reverberation are due to changes in theenvironmental parameters.

Task 28 also monitors the environmental parameters computed in task 26to determine if a new eigenray computational model and a new referencereverberation model need to be computed. When the environmentalparameters previously used in eigenray modeling task 30 and referencereverberation modeling task 32 differ from recently acquired parametersby a certain percentage, typically 15-20%, task 28 indicates that anenvironmental boundary has been identified. When a boundary isidentified by comparing recent environmental parameters, task 28 cannotify an operator who is given the option to override theidentification of an environmental boundary. To aid the operator inassessing the changes in environmental parameters, both a time historyof the most recent measurement points and the current values areavailable for display.

When an environmental boundary is identified, either by directcomparison of environmental parameters or by statistical analysismethod, task 28 notifies eigenray modeling task 30. Task 28 thenacquires a new baseline reverberation average for use in statisticalanalysis.

Task 28 operates as just described as long as the sonar system stateconfiguration remains constant. If, however, the sonar stateconfiguration has changed, sonar state processing task 24 will notifytask 28 of the sonar state change. Upon notification of any sonar stateparameter change that results in a reverberation energy envelope change,task 28 suspends and stores the current processing line-up. Task 28 thenestablishes a "new" baseline reverberation average for the current sonarstate configuration and begins processing "new" current averages forstatistical analysis. If sonar state processing task 24 then notifiestask 28 of a sonar state change that returns the sonar configuration toa previous system state, task 28 suspends and stores the "new"processing line-up and recalls and continues processing the previousprocessing line-up. Tracking sonar system state changes andcorresponding processing line-ups is done to ensure that changesdetected by statistical comparison of the current and baseline averagesare solely due to propagation environment changes and not a result ofchanges in the sonar state configuration.

Eigenray modeling task 30 uses the environmental parameters generated inenvironmental and navigational parameter processing task 26 to createsets of acoustic model eigenrays. A set of acoustic model eigenrays isdefined to be a set of rays that join a sound source to a given targetand predict the underwater acoustic propagation between the two points.Generating eigenrays is per se well known, and essentially requiressolving the reduced wave equation using one of several ray-basedacoustic wave-evaluation models such as the multipath expansion eigenraymodel, the FACT eigenray model, the RAYMODE eigenray model, or the like.More details on eigenray modeling are given in Weinberg, Application ofRay Theory to Acoustic Propagation in Horizontally Stratified Oceans, J.Acoust. Soc. of Amer., vol. 58, no. 1, 1975, pp. 97-109, incorporatedherein by reference, and Weinberg, Effective Range Derivative forAcoustic Propagation Loss in a Horizontally Stratified Ocean, J. Acoust.Soc. of Amer., vol. 70, no. 6, 1981, pp. 1736-1742, incorporated hereinby reference.

Sets of acoustic model eigenrays from the source to several points(targets) on the bottom at various ranges from the source are generated.Preferably, all the targets are in line with the source, separated by apredetermined range interval, and extend to an outside range equal tothe range over which reverberation is expected to be observed. Theoutside range can be estimated from the raw reverberation return orapproximated by considering the sound speed profile, depth, and sonarstate parameters. The range interval is chosen to be as large aspossible while meeting the requirement that the range be small enough toobtain sufficient resolution of the grazing angle, preferably resolutionof ±1°. This ensures that enough sets of eigenrays will be produced toenable accurate reference reverberation modeling and parameterextraction without generating an excessive number of eigenrays.

In generating the acoustic model eigenrays, the bottom reflectioncoefficient is set to near unity, and the surface reflection loss is setto unity. These settings are used to provide a reference set having manyeigenrays sufficient for use in the extraction processes. Additionally,setting the bottom reflection coefficient to near unity and the surfacereflection loss to unity allows for comparison between calculated andmeasured reverberation levels.

The eigenray amplitudes, source angles, receive angles (at the bottom),travel times, number of bottom or surface reflections or vertices, andphase are extracted from the eigenrays generated and are made availablefor use by reference reverberation modeling task 32 and for use bybottom parameter characterization task 34.

Eigenray modeling is initiated when task 30 is notified of a change inenvironmental parameters by reverberation and environmental parameterpost processing task 28 or of a change in the sonar state configurationby sonar state processing task 24. When eigenray modeling is initiatedas a result of environmental parameter changes, task 30 builds neweigenray models using the current environmental and sonar stateparameters. However, when task 30 is initiated as a result of sonarstate configuration changes, task 30 first stores the current eigenraysets and then either builds a new eigenray model or recalls a previouslystored eigenray model. Stored eigenray models are recalled if a sonarstate change returns the sonar configuration to a previous system stateand no environmental boundary has been detected since the eigenray modelhad been stored.

Reference reverberation modeling task 32 determines reverberation as afunction of time using the results from eigenray modeling task 30,environmental parameter processing task 26, and sonar state parameterprocessing task 24. Computing reference reverberation as a function oftime is well known in the art and essentially involves computing anindividual reverberation value and total travel time for every roundtrip path available between the source and each target and then addingtogether the individual reverberation values for all acoustic pathsintercepting the bottom whose total travel time falls within eachspecific time frame within a series of such time frames. Preferably,each time frame is chosen to have a duration approximately equal to 1/2the pulse length.

The individual reverberation values for each round trip path can begenerated in a known manner such as by calculating a transmitted rayscattering strength value for the eigenray from the source to thetarget, calculating a scattering strength value for the receivedeigenray, and multiplying these two values together. The transmittedeigenray scattering strength for an individual eigenray can becalculated using the following equation:

    TRNS.sub.STR =B.sub.STR *(SCRLVL*AMP.sub.EIG *BMP.sub.TRN).sup.2

where TRNS_(STR) is the transmitted eigenray scattering strength;B_(SMR) is the bottom scattering strength; SCRLVL is the source level;AMP_(EIG) is the eigenray amplitude; and BMP_(TRN) is the transmitterbeam pattern (pressure ratio) for the relative bearing and source angle.The receive eigenray scattering strength for an individual eigenray canbe calculated using the following equation:

    RCV.sub.STR =B.sub.STR *(AMP.sub.EIG *BMP.sub.RCV).sup.2

where RCV_(STR) is the received eigenray scattering strength; andBMP_(RCV) is the receiver beam pattern (voltage ratio) at the relativebearing and source angle.

Preferably, the transmitted eigenray scattering strength and the receiveeigenray scattering strength are calculated using either a unity bottomscattering strength or a Lambert's Rule scattering strength functionwith the coefficient set equal to unity to simplify the extraction ofthe bottom scattering strength coefficient and bottom loss.

Bottom parameter characterization task 34 can be performed using eitherthe probe pulse extraction method or the reverberation decay extractionmethod. Typically, for the probe pulse extraction method, the sonar isset to transmit a short pulse in a specific depression angle. A shortpulse length is chosen to reduce problems with the number of independentinterfering amplitudes from multiple scatterers and multiple paths whichcombine to form the returns. The duration of the pulse should be shortenough to enable sufficient resolution of the grazing angle, yet be longenough to enable one to accurately measure the returns. Preferably, thesonar is set to transmit a continuous wave pulse with a duration in therange of 10 to 20 milliseconds. The system is set to transmit the pulseat a depression angle, preferably between 10 and 30 degrees down. Thedepression angles are chosen giving consideration to the beam width suchthat there is an overlap of the area sonified between two consecutivedepression angles. For a beam width of 15°, specific depression anglesof 10, 20 and 30 degrees down can be chosen. Although any depressionangle within the preferred range can be used, selecting specificdepression angles reduces the number of transmitter and receiver beampatterns which need to be computed.

For the reverberation decay extraction method, the sonar system istypically set to transmit a continuous wave pulse having a duration inthe range of 450 to 550 ms. Preferably, the sonar system is aimed insubstantially horizontal direction (approximately 0° down).

For both methods, bottom parameter characterization task 34 includes twogeneral steps: defining extraction windows task 36 and extracting bottomparameters task 38. Defining extraction windows task 36 uses thereference reverberation calculated in task 32 and the measuredreverberation energy envelope as computed in task 22 or, alternatively,the current reverberation average as computed in reverberation andenvironmental parameter post processing task 28 to identify a period oftime, the extraction window, in which a reverberation energy envelopeexhibits properties which allows the extraction method to derive bottomparameters. Extracting bottom parameters task 38 performs either theprobe pulse or reverberation decay extraction algorithm, based on thetype of extraction window identified, to derive values for the bottomscattering coefficient and bottom loss.

The probe pulse method for bottom parameter extraction determines bottomscattering strength coefficient and bottom reflection loss versusgrazing angle from single, identifiable acoustic paths, if those pathsexist. More specifically, defining extraction windows task 36 of theprobe pulse method identifies time periods (extraction windows) whereinthe reverberation results solely or substantially from the direct (0surface bounces, 0 bottom bounces) path eigenrays or from the singlesurface, single bottom bounce (1,1) path eigenrays. Extracting bottomparameters task 38 derives the bottom scattering coefficient using timeperiods resulting from or dominated by a (0,0) path. Bottom loss iscomputed from the time periods resulting from or dominated by a (1,1)path.

To identify the time periods wherein the reverberation results solelyfrom or is dominated by contributions from the direct (0,0) or singlesurface bounce, single bottom bounce (1,1) eigenrays, definingextraction windows task 36 computes round trip travel time andindividual reverberation values for each 0,0 path and 1,1 path eigenray.The individual reverberation values should be calculated using the samemethod and parameters used by task 32 to calculate the referencereverberation.

Task 36 then subtracts each individual reverberation value from thereference reverberation time frame in which the round trip travel timeof that individual reverberation value falls. The extraction windows areidentified as the time frames for which the subtraction result equalszero. As previously discussed, the reference reverberation is typicallycomputed by adding together all individual reverberation values whoseround trip travel time falls within a specified time frame. Thus, fortime frames wherein an individual reverberation value equals thereference reverberation (subtract to zero), the reverberation for thattime frame (extraction window) results substantially from that single,identifiable eigenray path.

Bottom parameter extraction task 38 extracts a bottom scatteringstrength coefficient using the extraction windows, if any exist, whereinthe reverberation results from or is dominated by the 0,0 paths (bottomscattering time periods). The bottom scattering strength coefficient isdetermined by subtracting measured reverberation (dBs) from thereference reverberation (dBs) within each of the bottom scattering timeperiods (0,0 extraction windows) identified in task 36. This differencedirectly yields the scattering strength coefficient as a function ofthat extraction window, which can be converted to bottom scatteringstrength coefficient as a function of grazing angle by determining thegrazing angle for that window. The grazing angle for the extractionwindow is equal to the receive angle of the eigenray associated withthat extraction window. The eigenray receive angle is calculated ineigenray modeling task 30 described above. The results of the scatteringstrength determination vs. grazing angle can be displayed for review.Additionally, several different sonar depression angle results may beaccumulated to extend the grazing angle range.

Having determined the bottom scattering coefficient as a function ofgrazing angle, extracting bottom parameters task 38 can derive thebottom loss as a function of grazing angle if any extraction windowsexist in which the reverberation results solely or substantially fromthe single surface, single bottom bounce (1,1) path eigenrays. If anextraction window associated with a 1,1 path is identified (bottom losstime window), a predicted reverberation level is calculated for thatbottom loss time period. This predicted reverberation level iscalculated using the scattering strength coefficient vs. grazing angleresults previously computed by task 38. Preferably, the bottomscattering coefficient was calculated from the same reverberationenvelope from which bottom loss is being extracted. A direct subtractionof measured reverberation from predicted reverberation yields twice thesingle interaction bottom reflection loss vs. two way travel time.Travel time is converted to grazing angle by comparison to the traveltime versus grazing angle relationship determined for the eigenrayassociated with that extraction window derived in eigenray modeling task30.

The reverberation decay extraction method determines the bottomscattering strength coefficient and a single value for bottom loss basedon reverberation decay in shallow water environments where the acousticpropagation is governed by multiple interactions with the sea surfaceand ocean bottom. This method is predicated on the assumptions thatbottom loss is the dominant boundary loss mechanism and that a limitedrange of grazing angles at the bottom dominate the acoustic paths. Thus,a constant bottom reflection loss will be adequate to describe thepropagation. The reverberation decay method is particularly well suitedfor extracting bottom loss under low wind speed and downward refractingconditions. The method is especially applicable to the extraction ofbottom parameters from hull mounted sonar systems aimed in horizontaldirections in shallow water.

Defining extraction windows task 36 and extracting bottom parameterstask 38 of the reverberation decay method for parameter extraction areexplained with reference to FIG. 2 in which three sample reverberationtime histories (reverberation level, in dBs, as a function of time) areshown. Time history 60 represents the time history for the referencereverberation envelope computed in reference reverberation modeling task32, and time history 62 represents the time history for the measuredreverberation energy envelope computed in task 22 or, alternatively, thecurrent reverberation average computed in task 28. Time history 64 is areduced reverberation time history computed by subtracting referencereverberation time history 60 from the measured reverberation timehistory 62.

Defining extraction window task 36 for the reverberation decay methodisolates a time window (period of time) wherein the reverberationexhibits a characteristic decay rate. The extraction window isdetermined by analyzing the portion of time history 64 corresponding toreverberation. The portion of reduced reverberation time history 64corresponding to reverberation is isolated by separating reverberationtime history 62, which is directly related to time history 64, into aportion corresponding to noise and a portion corresponding toreverberation. To separate reverberation from noise in time history 62,the noise level on reverberation time history 62 must be determined.

The noise level is identified as the portion of time history 62 from theend of the time history for which a constant average reverberation levelis exhibited. The region of time history 62 labeled 62A isrepresentative of the portion of the time history for which a constantaverage reverberation level, taken to be the noise level, is exhibited.Reverberation is separated from the noise level by identifying the lasttime for which the reverberation level is greater than 10 dB above thenoise level. That is, starting from the end of time history 62, thehistory is traced back in time (moving to left in FIG. 2) until thereverberation level first reaches a level that is 10 dB higher than thenoise level. Point 62B identifies the last point in the time history 62where the reverberation level is 10 dB greater than the noise level, andpoint 64B indicates the corresponding point in time on the reducedreverberation time history 64. The portion of the reduced reverberationtime history corresponding to reverberation is from point 64B back intime (to the left in FIG. 2) along time history 64.

Having identified the portion of the reduced reverberation time historycorresponding to reverberation, the extraction window can be determined.The window is identified by using a standard linear least squaresanalysis or similar linear curve fitting analysis over a series ofextraction windows on time history 64, starting with a wide extractionwindow and narrowing the window until the linear least squares fit fromsuccessive windows show little change in slope or regressioncoefficient. Because the method is designed for shallow waterenvironments where the propagation is governed by multiple interactionswith the surface and bottom, the extraction window should coincide withthat portion of reduced reverberation time history 64 corresponding tomultiple surface and bottom interactions.

Preferably, the extraction window is determined by fixing a first edge(dotted line 66) of the window at a point on history 64 (preferably atthe end of the time history near point 64B) and a second edge (dottedline 68) at a second point on the time history (preferably at a pointshortly after the initial surface and bottom scattering are received). Alinear least squares fit to the portion of time history 64 bounded byedges 66 and 68 is calculated. The window is narrowed, preferably bysliding second edge 68 in the direction of arrow 70, a linear leastsquares fit for the new window is calculated, and the least squares fitfor the two most recent windows are compared. This process continuesuntil the linear least squares fit from two successive windows show nosignificant change (preferably, less than a 2% change) in slope orregression coefficient. The last window defined in this process becomesthe extraction window.

Extracting bottom parameters task 38 of the reverberation decay methodfocuses solely on the time period isolated by the extraction window todetermine a scattering strength coefficient and bottom loss. Thescattering strength coefficient is obtained from the intercept of theleast squares fit to time history 64 for the extraction window and thereverberation axis using the following equation:

    Intercept=10 log μ

where μ is the bottom scattering coefficient.

Bottom loss is derived by successively changing the value of bottomloss, using a binary search or similar technique, and recalculating thereference reverberation model over the time period defined by theextraction window for each new value of bottom loss until the slope ofthe recalculated reference reverberation closely matches the slope ofthe least squares fit to time history 64. To speed the real-timeprocess, previously derived values of bottom loss are used as a startingpoint for the binary search, under the assumption that bottom lossshould not be a rapidly changing function of position.

Extracting bottom parameters task 38 determines the dominant grazingangle at the bottom for which the bottom loss applies by averaging thegrazing angles of the eigenrays intercepting the bottom, weighted bytheir amplitude, for all eigenray pairs whose round trip travel timenearly equals the time at the center of the extraction window.

The values of bottom scattering coefficient and bottom loss thus derivedcan then be entered back into the model to calculate a total referencereverberation time history for comparison to the measured reverberation.This allows an operator to see the amount of beam reverberation which isadequately modeled under the assumptions implicit in the method, andmake an assessment of the range for which the results can be usedreliably in predicting propagation in the shallow water environment.

Referring now to FIG. 3, there is shown a block diagram illustrating thefunctional units of a preferred embodiment of parameter extractionprocessor 100 for the real-time extraction of ocean properties inaccordance with the present invention. Data conversion andsynchronization unit 110 accepts sonar, navigational, and environmentalsensor data from a data acquisition system (not shown). Data conversionand synchronization unit 110 performs data stream synchronization bysampling continuous serial data, converting the serial data to byte wideparallel data, and transmitting the data to data demultiplexing, signalconditioning and signal processing unit 120.

Data demultiplexing, signal conditioning and signal processing unit 120accepts data from unit 110. The data is demultiplexed and the raw timedomain samples of the acoustic signal are separated from theenvironmental, navigational, and sonar state data. Processing unit 120processes the raw time domain samples of the acoustic signal into areverberation energy envelope in the frequency band of interest which isthen sent to system manager unit 130. The remaining raw data blocks fromthe environmental, navigational, and sonar state sensors are grouped bytype and also passed to system manager unit 130.

System manager unit 130 is the primary control unit responsible for allenvironmental, navigational, and sonar data processing. Unit 130controls the data demultiplexing, signal conditioning and processing,and data transfer tasks associated with data demultiplexing, signalconditioning and signal processing unit 120. System manager unit 130processes the raw data blocks containing navigational, environmental andsonar state data into valid system state messages and conditionedenvironmental data for use by other units. System manager unit 130 alsoperforms environmental and reverberation post processing on the acousticsignal and environmental data, wherein the reverberation envelopes areanalyzed in an attempt to detect environmental and acoustic propagationboundaries.

System manager unit 130 also performs system coordination by monitoringthe results of the boundary detection processing as well as the sonarsystem state data. System manager unit 130 uses the results of theboundary detection processing and the sonar state data to determine thefunctional flow and operation of the processor. Functional flowdecisions such as when to build eigenray models or which extractionmethod to perform are passed as commands to local modeling andextraction system manager unit 140.

In addition, system manager unit 130 provides a user interface whichallows a user to view system state messages, conditioned data, andprocessing results. Based on this information, the user can modify oroverride function flow decisions.

Local modeling and extraction system manager unit functions as theprimary information manager and task manager for eigenray andreverberation modeling unit 150, reverberation decay extractionprocessing and reverberation modeling unit 160, and probe pulseextraction processing and reverberation modeling unit 170. In responseto commands (functional flow decisions), conditioned data, and systemstate messages received from unit 130, local modeling and extractionsystem manager unit 140 provides data to and concurrently controls andcoordinates the processing flow across modeling and extraction units150-170.

Unit 150 performs eigenray modeling. Unit 160 performs reverberationdecay extraction processing. Unit 170 performs probe pulse extractionprocessing. Units 150, 160, and 170 compute reference reverberationmodels concurrently. Together, modeling and extraction units 150-170perform all high speed, high fidelity eigenray and reverberationmodeling, as well as, execution of ocean bottom property extractionalgorithms between modeled information and real-time acousticreverberation data. The results from modeling and extraction units150-170 are returned to system manager unit 130, via local modeling andextraction system manager unit 140, for display.

Referring now to FIG. 4, there is shown a schematic diagram of thehardware elements, system enclosures and data paths, along with thefunctions each performs, for parameter extraction processor 100 inaccordance with the present invention. In FIG. 4, a hardware elementhaving the same reference number as a functional unit of FIG. 3 is usedto perform that function as described in reference to FIG. 3. Theprocessor shown in FIG. 4 provides a platform on which the tasks of FIG.1 are executed to monitor acoustic propagation environments and to modelreverberation and extract boundary parameters from real-time sonarreverberation. The operation of extraction processor 100 will beexplained with additional reference to FIG. 1.

Data conversion and synchronization unit 110 is provided to synchronizeand convert data for transfer between a data acquisition system (notshown) and digital signal processor board 120. An interface boardconverts continuous serial data, running at approximately 11.2 Mbits persecond, to byte wide parallel data. The data is formatted as requiredfor input to data demultiplexing, signal conditioning and signalprocessing unit 120. The data is buffered and burst transmitted to unit120.

Unit 120 can be an Ariel V-C40 Hydra digital signal processing board orthe like provided to perform the data demultiplexing and signalconditioning functions of data correlation task 20 and the reverberationprocessing function of reverberation processing task 22, through digitalsignal processors 120a and 120b, respectively. Unit 120 comprises abyte-wide parallel communications port (not shown), digital signalprocessors 120a and 120b (such as two TMS320C40 DSPs or the like), and aglobal bank of dynamic random access memory (not shown).

Unit 120 accepts data from unit 110 via the byte-wide 23 parallelcommunications port. Signal processor 120a demultiplexes the real-timedata stream and groups the data in two forms: acoustic beam data and rawdata blocks containing navigational, environmental, and sonar systemstate data. Signal processor 120a also time correlates the data suchthat the system/sensor data samples and the acoustic signal data samplescan be tracked and related to one another over a common time domain.

The sonar acoustic beam data is processed into reverberation energyenvelopes, as described in reference to reverberation processing task 22of FIG. 1, by signal processor 120b. The reverberation energy envelopes,along with the raw data blocks, are passed to the global bank of dynamicrandom access memory (not shown), a common block of shared memory thatis available to both signal processors 120a and 120b and accessible bysystem manager unit 130.

Unit 130 can be a general purpose computer and is the primaryworkstation or similar device for general purpose computer system 180such as a Navy standard TAC-II computer system (SUN4/330) or the like.Unit 130 comprises a digital signal processor (DSP) manager 130a, asystem dispatcher 130b, a reverberation and environmental sensor datapost processor 130c, and a graphical user interface 130d. Units 120 and130 are both running in a single VME enclosure on general purposecomputer system 80.

Unit 130 is configured to access unit 120 through a standardtransmission channel such as a VME bus (not shown). DSP manager 130acontrols the data demultiplexing and time correlation performed bysignal processor 120a, as well as the reverberation processing performedby signal processor 120b. DSP manager 130a also controls the datatransfer from unit 120. DSP manager 130a receives the reverberationenergy envelopes and the raw data blocks of environmental, navigational,and sonar state data from unit 120 and passes both the reverberationenvelopes and the raw data blocks into shared memory segment 132.

System dispatcher 130b processes the raw data blocks placed in sharedmemory segment 132 by DSP manager 130a into valid sonar system state andenvironmental sensor data performing the operations of sonar stateparameter processing task 24 and environmental and navigationalparameter processing task 26 of FIG. 1. System dispatcher 130b passesthe valid sonar state and environmental data to shared memory 132 foraccess by reverberation processor 130c and user interface 130d. Systemdispatcher 130b also monitors the sonar state data and the resultsreverberation processor 130c and user interface 130d, discussed below,to determine the overall functional flow and operation of processor 100of FIG. 4

Reverberation and environmental sensor data post processor 130c attemptsto detect environmental and acoustic boundaries by correlatingcharacteristic changes in the reverberation energy envelope withenvironmental parameters, as well as by monitoring the environmentalparameters for change by performing the functions of reverberation andenvironment parameter post processing task 28 of FIG. 1. Graphical userinterface 130d provides interactive display processing which allows anoperator to view valid sonar state messages, conditioned data, andprocessing results and to provide system input such as an input tomodify or override processing options.

Local modeling and extraction system manager 140 can be a MotorolaMVME167-68040 or the like. Extraction system manager 140 performsprovides data to and concurrently controls and coordinates the data flowand the processing flow across modeling and extraction units 150-170.Modeling and extraction units 150-170 are commercially availableprocessor boards such as CSPI supercards (SC-2XL(I860)) or the like.Together extraction system manager 140 and the processor boards of units150-170 combine to configure a commercially available CSPI RTS-860system (modeling processor 182).

The processor board of unit 150 performs the eigenray modeling functiondescribed in task 30 of FIG. 1. and generates a first reverberationmodel. The processor board of unit 160 determines the bottom scatteringstrength coefficient and a single value for bottom loss performing boththe defining extraction windows task 36 and the extracting bottomparameters task 38 of the reverberation decay method for bottomparameter characterization. Further, the unit 160 processor boardgenerates a second reverberation model. The processor board of unit 170determines the bottom scattering strength coefficient and the bottomloss versus grazing angle using the steps outlined in definingextraction windows task 36 and extracting bottom parameters task 38 ofthe probe pulse method for bottom parameter characterization. The unit170 processor board also generates a third reverberation model. Modelingprocessor 182 uses the reverberation models generated by units 150-170to produce reference reverberation as a function of time performing thesteps described in reference reverberation modeling task 32. Typically,reference reverberation as a function of time is generated concurrentlyby units 150-170 wherein each unit generates a different portion of thereference reverberation function. For example, the referencereverberation can be divided into three equal time segments such that,if the reference reverberation function extends over 12 seconds, each ofthe processor boards in modeling processor 182 builds a separate foursecond segment of the reference reverberation function.

Environmental and sonar state data, reverberation energy envelopes,processing instructions, and processing results are transferred betweengeneral purpose computer system 180 and modeling processor 182 via aTCP/IP or similar connection through transmission means 184 such as anethernet network or the like.

Referring now to FIG. 5, there is shown a block diagram of system 200for the extraction of ocean bottom properties in real-time in accordancewith the present invention. In FIG. 5, environmental sensors 202 provideinformation such as relative wind direction and velocity (from ananemometer or similar weather station) and water temperature and depthdata (from an XBT or similar device) and sound speed measurements (froma velocity meter or similar device). Navigation system 204 such as a GPSsystem or the like provides information such as ship positioncoordinates, ship heading, and ship speed. Sonar system 206 providessonar system state data 208 and acoustic beam data 210. Sonar systemstate data 208 and data from navigation system 204 and environmentalsensors 202 are monitored by communication server 212. Communicationserver 212 continuously monitors and collects the environmental,navigational, and sonar system state data and generates data stream 214.Preferably, data stream 214 comprises environmental, navigational, andsonar state data sampled at continuous periodic rates based upon theexpected rate of change of the data and upon the consistency andaccuracy of instantaneous data samples. Reasonable periodic rates areonce every second for navigational, environmental and sonar state datasampling.

Data acquisition system 216 receives data stream 214 from communicationserver 212 and acoustic beam data 210 from sonar system 208 andgenerates serial data stream 218. Data acquisition system 216 which,together with communication sever 212, can be from a standard activesonar performance realization (ASPR) system or the like multiplexesacoustic beam data 210 and data stream 218 into serial data stream 218which becomes the input data stream to parameter extraction processor100. Parameter extraction processor 100 operates as described above inreference to FIGS. 3 and 4.

In operation, environmental sensors 202, navigation system 204, andsonar system 206 provide continuous serial data streams to communicationserver 212. Typically, communication server 212 contains multiple serialdata ports for receiving the serial asynchronous data streams.Communication server 212 receives the multiple data streams and builds asingle output data stream 214 which is passed to data acquisition system216. In building data stream 214, communication server 212 formats thedata received from environmental sensors 202, navigation system 204, andsonar system 206 into the format required by data acquisition system216. Data acquisition system 216 receives acoustic beam data 210 anddata stream 214 from communication server 212 and multiplexes the datato form a single serial data stream 218. Output data stream 218 isformatted for output to a magnetic tape data storage means (not shown)and is also passed to data conversion and synchronization unit 110 ofprocessor 100. (More details on the formats of data stream 214 ofcommunication server 212 and data stream 218 from data acquisitionsystem 216 are given in Acoustic Data Recorder Specification, NavalUnderwater Systems Center report, Dec. 18, 1989 incorporated herein byreference and in Interface Design Documentation for the ASPR DataAcquisition System, Naval Undersea Warfare Center Report, Jul. 31, 1992incorporated herein by reference.)

What has thus been described is a novel method for the extraction ofocean bottom parameters in real-time which offers several significantadvantages over prior art methods. First, the method provides anaccurate way to determine bottom loss and bottom scattering coefficientvalues in real-time. Second, the present invention provides a method toextract values for bottom loss and the bottom scattering coefficient inreal-time by processing measured reverberation and time correlatedsonar, navigational, and environmental data.

Obviously many modifications and variations of the present invention maybecome apparent in light of the above teachings. In light of the above,it is therefore understood that within the scope of the appended claims,the invention may be practiced otherwise than as specifically described.

What is claimed is:
 1. A method for extraction of ocean bottomparameters in real-time comprising the steps of:periodically determiningenvironmental parameters; periodically determining a sonar system stateconfiguration; periodically obtaining a reverberation energy envelope;monitoring the environmental parameters and the current sonar systemstate configuration for changes; creating an eigenray computation modelin response to changes in the environmental parameters or changes in thesonar system state configuration; generating a reference reverberationmodel using the eigenray computation model; and characterizing oceanbottom parameters from the reverberation energy envelope by using theeigenray computation model and the reference reverberation model. PG,442. The method of claim 1, wherein the characterizing ocean bottomparameters step further comprises the steps of:identifying periods oftime in which the reverberation energy envelope exhibits characteristicproperties; and extracting bottom parameters by processing portions ofthe reverberation energy envelope and the reference reverberation modelfalling within the identified periods of time.
 3. The method of claim 2,wherein the identifying periods of time step further comprises the stepsof:identifying bottom scattering time periods; and identifying bottomloss time periods.
 4. The method of claim 3, wherein the extractingbottom parameters step further comprises the steps of:calculating abottom scattering coefficient by comparing the reverberation energyenvelope with the reference reverberation model for each bottomscattering time period; and calculating a bottom loss value bycalculating a predicted reverberation value, using the results from thecalculating a bottom scattering coefficient step, for each bottom losstime period and comparing the reverberation energy envelope with thepredicted reverberation value for each bottom scattering time period. 5.The method of claim 2, wherein the identifying periods of time stepfurther comprises the steps of:separating the reverberation energyenvelope into a portion representing noise and a portion representingreverberation; calculating a reduced reverberation envelope bysubtracting the portion of the reverberation envelope representingreverberation; and identifying a decay time period wherein the reducedreverberation envelope exhibits a linear decay rate.
 6. The method ofclaim 5, wherein the extracting bottom parameters step further comprisesthe steps of:calculating a bottom scattering strength coefficient byobtaining the intercept of a straight line fit to the reducedreverberation within the decay time period; and calculating a bottomloss value by successively changing the value of bottom loss andrecalculating the reference reverberation model within the decay timeperiod until the slope of the recalculated reference reverberationclosely matches the slope of the straight line fit to the reducedreverberation within the decay time period.
 7. The method of claim 2,wherein the monitoring the environmental parameters and the currentsonar system state configuration for changes step further comprises thesteps of:calculating a baseline reverberation average; calculating acurrent reverberation average; and comparing the baseline reverberationaverage with the current reverberation average to identify variationsbetween the baseline reverberation average and the current reverberationaverage.
 8. The method of claim 7, wherein the identifying periods oftime step further comprises the steps of:identifying bottom scatteringtime periods; and identifying bottom loss time periods.
 9. The method ofclaim 8, wherein the extracting bottom parameters step further comprisesthe steps of:calculating a bottom scattering coefficient by comparingthe reverberation energy envelope with the reference reverberation modelfor each bottom scattering time period; and calculating a bottom lossvalue by calculating a predicted reverberation value, using the resultsfrom the calculating a bottom scattering coefficient step, for eachbottom loss time period and comparing the reverberation energy envelopewith the predicted reverberation value for each bottom scattering timeperiod.
 10. The method of claim 7, wherein the identifying periods oftime step further comprises the steps of:separating the reverberationenergy envelope into a portion representing noise and a portionrepresenting reverberation; calculating a reduced reverberation envelopeby subtracting the portion of the reverberation envelope representingreverberation; and identifying a decay time period wherein the reducedreverberation envelope exhibits a linear decay rate.
 11. The method ofclaim 10, wherein the extracting bottom parameters step furthercomprises the steps of:calculating a bottom scattering strengthcoefficient by obtaining the intercept of a straight line fit to thereduced reverberation within the decay time period; and calculating abottom loss value by successively changing the value of bottom loss andrecalculating the reference reverberation model within the decay timeperiod until the slope of the recalculated reference reverberationclosely matches the slope of the straight line fit to the reducedreverberation within the decay time period.